autonomousvision/gaussian-opacity-fields

Health v2 · maintenance only

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Adoption not part of the score

1017 stars · 69 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Gaussian Opacity Fields (GOF) is a Python/CUDA research implementation for efficient, adaptive surface reconstruction in unbounded scenes using 3D Gaussian Splatting. It extracts geometry directly from 3D Gaussians by identifying their level set and uses Marching Tetrahedra for compact, scene-adaptive mesh extraction.

Use cases

• reconstruct 3D surfaces from multi-view images with gaussian splatting
• extract meshes from 3D gaussian splatting scenes
• novel view synthesis on unbounded scenes like Mip-NeRF 360
• reconstruct surfaces of Tanks and Temples or DTU scenes
• compare 3DGS-based surface reconstruction against neural implicit methods
• train gaussian splatting models with geometry regularization

When to choose

• you need high-quality mesh extraction from 3D Gaussians without Poisson reconstruction or TSDF fusion
• you want fast training and adaptive, compact meshes in unbounded scenes
• you are doing research on gaussian splatting surface reconstruction and want a SIGGRAPH Asia 2024 baseline

When to avoid

• you only need novel view rendering without geometry or meshes
• you lack a CUDA GPU or cannot build custom CUDA submodules
• you need a production-ready tool with a polished UI rather than research code

Facets

library · maturity active

computer-vision image-processing graphics machine-learning computer-vision graphics machine-learning python gaussian-splatting 3d-reconstruction surface-reconstruction novel-view-synthesis marching-tetrahedra mesh-extraction research-code linux gpu

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markdown · JSON · MCP: product_card(name="autonomousvision/gaussian-opacity-fields")